Bibliografías temáticas / Regression analysis

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Literatura académica sobre el tema "Regression analysis"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 25 de julio de 2025

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Artículos de revistas sobre el tema "Regression analysis"

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Asakura, Koko, and Toshimitsu Hamasaki. "Regression analysis." Drug Delivery System 31, no. 1 (2016): 72–81. http://dx.doi.org/10.2745/dds.31.72.

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Anthony, Denis. "Regression analysis." Nurse Researcher 4, no. 4 (1997): 54–62. http://dx.doi.org/10.7748/nr.4.4.54.s6.

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Anthony, Denis. "Regression analysis." Nurse Researcher 4, no. 1 (1996): 318–26. http://dx.doi.org/10.7748/nr1996.10.4.1.318.c6066.

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Munro, Barbara Hazard. "Regression Analysis." Clinical Nurse Specialist 6, no. 2 (1992): 77. http://dx.doi.org/10.1097/00002800-199200620-00006.

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Rawles, John, and J. C. Bignall. "REGRESSION ANALYSIS." Lancet 327, no. 8481 (1986): 614–15. http://dx.doi.org/10.1016/s0140-6736(86)92832-1.

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Bland, J. M., and D. G. Altman. "REGRESSION ANALYSIS." Lancet 327, no. 8486 (1986): 908–9. http://dx.doi.org/10.1016/s0140-6736(86)91008-1.

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Glaser, Elton. "Regression Analysis." Missouri Review 27, no. 1 (2004): 140–41. http://dx.doi.org/10.1353/mis.2004.0010.

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Lewis, S. "Regression analysis." Practical Neurology 7, no. 4 (2007): 259–64. http://dx.doi.org/10.1136/jnnp.2007.120055.

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Bolshakova, Lyudmila Valentinovna. "Correlation and Regression Analysis of Economic Problems." Revista Gestão Inovação e Tecnologias 11, no. 3 (2021): 2077–88. http://dx.doi.org/10.47059/revistageintec.v11i3.2074.

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Srivastava, Vaibhav, Saksham Kaushik, and Ujjwal Raj. "Student Placement Package Prediction By Regression Analysis." International Journal of Research Publication and Reviews 5, no. 1 (2024): 4724–29. http://dx.doi.org/10.55248/gengpi.5.0124.0345.

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Tesis sobre el tema "Regression analysis"

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Sullwald, Wichard. "Grain regression analysis." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86526.

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Thesis (MSc)--Stellenbosch University, 2014.<br>ENGLISH ABSTRACT: Grain regression analysis forms an essential part of solid rocket motor simulation. In this thesis a numerical grain regression analysis module is developed as an alternative to cumbersome and time consuming analytical methods. The surface regression is performed by the level-set method, a numerical interface advancement scheme. A novel approach to the integration of the surface area and volume of a numerical interface, as defined implicitly in a level-set framework, by means of Monte-Carlo integration is proposed. The gra
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Dai, Elin, and Lara Güleryüz. "Factors that influence condominium pricing in Stockholm: A regression analysis : A regression analysis." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254235.

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This thesis aims to examine which factors that are of significance when forecasting the selling price of condominiums in Stockholm city. Through the use of multiple linear regression, response variable transformation, and a multitude of methods for refining the model fit, a conclusive, out of sample validated model with a confidence level of 95% was obtained. To conduct the statistical methods, the software R was used. This study is limited to the districts of inner city Stockholm with the postal codes 112-118, and the final model can only be applied to this area as the postal codes are includ
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Zuo, Yanling. "Monotone regression functions." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29457.

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In some applications, we require a monotone estimate of a regression function. In others, we want to test whether the regression function is monotone. For solving the first problem, Ramsay's, Kelly and Rice's, as well as point-wise monotone regression functions in a spline space are discussed and their properties developed. Three monotone estimates are defined: least-square regression splines, smoothing splines and binomial regression splines. The three estimates depend upon a "smoothing parameter": the number and location of knots in regression splines and the usual [formula omitted] in smo
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Ryu, Duchwan. "Regression analysis with longitudinal measurements." Texas A&M University, 2005. http://hdl.handle.net/1969.1/2398.

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Bayesian approaches to the regression analysis for longitudinal measurements are considered. The history of measurements from a subject may convey characteristics of the subject. Hence, in a regression analysis with longitudinal measurements, the characteristics of each subject can be served as covariates, in addition to possible other covariates. Also, the longitudinal measurements may lead to complicated covariance structures within each subject and they should be modeled properly. When covariates are some unobservable characteristics of each subject, Bayesian parametric and nonparametric re
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Campbell, Ian. "The geometry of regression analysis." Thesis, University of Ottawa (Canada), 1989. http://hdl.handle.net/10393/5755.

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Wiencierz, Andrea. "Regression analysis with imprecise data." Diss., Ludwig-Maximilians-Universität München, 2013. http://nbn-resolving.de/urn:nbn:de:bvb:19-166786.

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Statistical methods usually require that the analyzed data are correct and precise observations of the variables of interest. In practice, however, often only incomplete or uncertain information about the quantities of interest is available. The question studied in the present thesis is, how a regression analysis can reasonably be performed when the variables are only imprecisely observed. At first, different approaches to analyzing imprecisely observed variables that were proposed in the Statistics literature are discussed. Then, a new likelihood-based methodology for regression analysis wit
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Jeffrey, Stephen Glenn. "Quantile regression and frontier analysis." Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/47747/.

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In chapter 3, quantile regression is used to estimate probabilistic frontiers, i.e. frontiers based on the probability of being dominated. The results from the empirical application using an Italian hotel dataset show rejections of a parametric functional form and a location shift effect, large uncertainty of the estimates of the frontier and wide confidence intervals for the estimates of efficiency. Quantile regression is further developed to estimate thick probabilistic frontiers, i.e. frontiers based on a group of efficient firms. The empirical results show that the differences between the
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Ranganai, Edmore. "Aspects of model development using regression quantiles and elemental regressions." Thesis, Stellenbosch : Stellenbosch University, 2007. http://hdl.handle.net/10019.1/18668.

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Dissertation (PhD)--University of Stellenbosch, 2007.<br>ENGLISH ABSTRACT: It is well known that ordinary least squares (OLS) procedures are sensitive to deviations from the classical Gaussian assumptions (outliers) as well as data aberrations in the design space. The two major data aberrations in the design space are collinearity and high leverage. Leverage points can also induce or hide collinearity in the design space. Such leverage points are referred to as collinearity influential points. As a consequence, over the years, many diagnostic tools to detect these anomalies as well as alt
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Lo, Sau Yee. "Measurement error in logistic regression model /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?MATH%202004%20LO.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.<br>Includes bibliographical references (leaves 82-83). Also available in electronic version. Access restricted to campus users.
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Kulich, Michal. "Additive hazards regression with incomplete covariate data /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/9562.

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Libros sobre el tema "Regression analysis"

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Sen, Ashish, and Muni Srivastava. Regression Analysis. Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-4470-7.

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Sen, Ashish, and Muni Srivastava. Regression Analysis. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-25092-1.

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Lewis-Beck, Michael S. Regression analysis. Sage, 1994.

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Draper, N. R. Applied regression analysis. 3rd ed. Wiley, 1998.

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1923-, Smith Harry, ed. Applied regression analysis. 3rd ed. Wiley, 1998.

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Schroeder, Larry, David Sjoquist, and Paula Stephan. Understanding Regression Analysis. SAGE Publications, Inc., 1986. http://dx.doi.org/10.4135/9781412986410.

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Westfall, Peter H., and Andrea L. Arias. Understanding Regression Analysis. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781003025764.

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von Rosen, Dietrich. Bilinear Regression Analysis. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78784-8.

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Rawlings, John O., Sastry G. Pantula, and David A. Dickey, eds. Applied Regression Analysis. Springer-Verlag, 1998. http://dx.doi.org/10.1007/b98890.

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Thrane, Christer. Applied Regression Analysis. Routledge, 2019. http://dx.doi.org/10.4324/9780429443756.

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Capítulos de libros sobre el tema "Regression analysis"

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Arkes, Jeremy. "Summarizing thoughts." In Regression Analysis, 2nd ed. Routledge, 2022. http://dx.doi.org/10.4324/9781003285007-14.

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Arkes, Jeremy. "Time-series models." In Regression Analysis, 2nd ed. Routledge, 2022. http://dx.doi.org/10.4324/9781003285007-10.

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Arkes, Jeremy. "Regression analysis basics." In Regression Analysis, 2nd ed. Routledge, 2022. http://dx.doi.org/10.4324/9781003285007-2.

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Arkes, Jeremy. "Methods to address biases." In Regression Analysis, 2nd ed. Routledge, 2022. http://dx.doi.org/10.4324/9781003285007-8.

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Arkes, Jeremy. "Introduction." In Regression Analysis, 2nd ed. Routledge, 2022. http://dx.doi.org/10.4324/9781003285007-1.

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Arkes, Jeremy. "What could go wrong when estimating causal effects?" In Regression Analysis, 2nd ed. Routledge, 2022. http://dx.doi.org/10.4324/9781003285007-6.

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Arkes, Jeremy. "Strategies for other regression objectives." In Regression Analysis, 2nd ed. Routledge, 2022. http://dx.doi.org/10.4324/9781003285007-7.

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Arkes, Jeremy. "What does “holding other factors constant” mean?" In Regression Analysis, 2nd ed. Routledge, 2022. http://dx.doi.org/10.4324/9781003285007-4.

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Arkes, Jeremy. "How to conduct a research project." In Regression Analysis, 2nd ed. Routledge, 2022. http://dx.doi.org/10.4324/9781003285007-12.

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Arkes, Jeremy. "The ethics of regression analysis." In Regression Analysis, 2nd ed. Routledge, 2022. http://dx.doi.org/10.4324/9781003285007-13.

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Actas de conferencias sobre el tema "Regression analysis"

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Swindell, Jonathan E., Carson Slater, Samuel Hussey, Charles Baylis, and Robert J. Marks. "Assessing Interference with Regression Analysis Techniques." In 2024 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems (WMCS). IEEE, 2024. http://dx.doi.org/10.1109/wmcs62019.2024.10619025.

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Al Siddik Shammo, Basid, and Biswaranjan Acharya. "Esports Earning Analysis Using Regression Model." In 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). IEEE, 2024. https://doi.org/10.1109/icaiqsa64000.2024.10882303.

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Primantara, Ari, Udisubakti Ciptomulyono, and Berlian Al Kindhi. "Bagging System Performance Analysis Using Artificial Neural Network, Random Forest Regression, Linear Regression, and Support Vector Regression." In 2024 IEEE International Symposium on Consumer Technology (ISCT). IEEE, 2024. https://doi.org/10.1109/isct62336.2024.10791247.

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Saragih, Jason. "Principal regression analysis." In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2011. http://dx.doi.org/10.1109/cvpr.2011.5995618.

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Chivukula, V. N. Aditya Datta, and Sri Keshava Reddy Adupala. "Music Signal Analysis: Regression Analysis." In 2nd International Conference on Machine Learning, IOT and Blockchain (MLIOB 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111205.

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Machine learning techniques have become a vital part of every ongoing research in technical areas. In recent times the world has witnessed many beautiful applications of machine learning in a practical sense which amaze us in every aspect. This paper is all about whether we should always rely on deep learning techniques or is it really possible to overcome the performance of simple deep learning algorithms by simple statistical machine learning algorithms by understanding the application and processing the data so that it can help in increasing the performance of the algorithm by a notable amo
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Khan, Mohiuddeen, and Kanishk Srivastava. "Regression Model for Better Generalization and Regression Analysis." In ICMLSC 2020: The 4th International Conference on Machine Learning and Soft Computing. ACM, 2020. http://dx.doi.org/10.1145/3380688.3380691.

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Podgurski, Andy. "Session details: Regression testing." In ISSTA '08: International Symposium on Software Testing and Analysis. ACM, 2008. http://dx.doi.org/10.1145/3260628.

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van Erp, N., and P. van Gelder. "Bayesian logistic regression analysis." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 32nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2013. http://dx.doi.org/10.1063/1.4819994.

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Krasotkina, O., and V. Mottl. "Adaptive nonstationary regression analysis." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761666.

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Zhang, Wenshan. "Life Expectancy Regression Analysis." In Proceedings of the 3rd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2024, May 24–26, 2024, Jinan, China. EAI, 2024. http://dx.doi.org/10.4108/eai.24-5-2024.2350121.

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Informes sobre el tema "Regression analysis"

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Nesheim, Lars. Regression Analysis. The IFS, 2011. http://dx.doi.org/10.1920/ps.ifs.2024.0922.

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Burke, Kevin. Regression in Analysis. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada494895.

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Meloncelli, Daniel. Regression Analysis using R. Instats Inc., 2025. https://doi.org/10.61700/j3s1r521de2ee1472.

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This seminar provides a comprehensive introduction to regression analysis using R. Participants will explore the fundamentals of correlation, simple linear regression, multiple regression, and logistic regression. The seminar emphasises practical application, guiding attendees through data preparation, model fitting, assumption checking, and interpretation of results. Hands-on sessions will enable participants to apply regression techniques to real-world datasets, enhancing their analytical skills for research purposes.
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Meloncelli, Daniel. Regression Analysis using SPSS. Instats Inc., 2025. https://doi.org/10.61700/9zohmz8j1gzcj1476.

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This seminar provides a comprehensive introduction to regression analysis using SPSS, equipping researchers with the skills to apply linear and logistic regression techniques in their work. Participants will gain practical experience in model fitting, assumption checking, and interpreting results, enhancing their ability to leverage data effectively in their respective fields.
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Crowson, Michael. Regression and Moderation Analysis with PROCESS in SPSS. Instats Inc., 2022. http://dx.doi.org/10.61700/7h5yxd5cms1i1469.

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This seminar, taught by Dr. Mike Crowson, is designed to introduce students and researchers to the basics of linear regression analysis using SPSS, including automated moderation analysis (interaction effect tests) using the PROCESS macro. The seminar is designed for anyone who wishes to better understand linear regression concepts and assumptions and how to perform regression analyses involving both quantitative and qualitative predictors with moderation effects. In addition to developing practical skills setting up regression models, testing hypotheses, and generating results, a central focu
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Marchese, Malvina. Regression Analysis: Everything You Need to Know. Instats Inc., 2023. http://dx.doi.org/10.61700/758g2bp367fhc469.

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Regression is one of the most important modelling tools used in a variety of different research fields. In many cases, the plausibility of an empirical finding depends on the robustness of the regression results. This two-day seminar offers an in-depth introduction to linear regression models for cross sectional and time series data, covering all aspects of regression modelling, from model and variables selection, to dummy variables and multicollinearity and endogeneity to prediction (in sample and out of sample). An official Instats certificate of completion is provided at the conclusion of t
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Marchese, Malvina. Regression Analysis: Everything You Need to Know. Instats Inc., 2023. http://dx.doi.org/10.61700/s16tb8g7d585g469.

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Regression is one of the most important modelling tools used in a variety of different research fields. In many cases, the plausibility of an empirical finding depends on the robustness of the regression results. This two-day seminar offers an in-depth introduction to linear regression models for cross sectional and time series data, covering all aspects of regression modelling, from model and variables selection, to dummy variables and multicollinearity and endogeneity to prediction (in sample and out of sample). An official Instats certificate of completion is provided at the conclusion of t
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Landis, Ronald. Regression Analysis for Social and Health Science. Instats Inc., 2023. http://dx.doi.org/10.61700/me4ohse6jsdoy469.

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This comprehensive 2-day workshop will equip PhD students, professors, and professional researchers with the knowledge and skills needed to effectively use multiple regression analysis in their research. Participants will gain a deep understanding of multiple regression, learn how to use R, Excel, and Jamovi for analysis, interpret and report results, handle missing data, and enhance their research capabilities. An official Instats certificate of completion and 2 ECTS Equivalent points are provided at the conclusion of the seminar.
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Steed, Chad A., J. Edward SwanII, Patrick J. Fitzpatrick, and T. J. Jankun-Kelly. A Visual Analytics Approach for Correlation, Classification, and Regression Analysis. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1035521.

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Jakobsen, Tor Georg. Using ChatGPT with Stata: Regression Analysis (Part II). Instats Inc., 2025. https://doi.org/10.61700/2w5b85mmn916t1568.

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This comprehensive 3-day workshop focuses on refining statistical modeling and regression analysis skills by integrating advanced AI tools like ChatGPT with traditional software such as Stata. Participants will gain practical competencies in various regression techniques, including interaction effects (or 'moderation'), logistic regression, missing data, scale construction, transformations, weighting, and how to deal with regression assumptions, along with AI-powered assistance from ChatGPT to clarify concepts and troubleshoot commands.
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